The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: Hierarchical Model-Based Estimation Approach
Version: 1.1
Date: 2020-05-06
Description: For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016)<doi:10.1007/s13595-016-0590-1> and Saarela et al. (2018) <doi:10.3390/rs10111832>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
LazyData: TRUE
Imports: Rcpp (≥ 0.12.16)
Depends: methods, stats, R (≥ 3.5)
LinkingTo: Rcpp, RcppArmadillo
Collate: RcppExports.R helper_functions.R HMB-class.R SummaryHMB-class.R ghmb.R gtsmb.R hmb.R tsmb.R
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2020-05-06 06:36:16 UTC; svla0001
Author: Svetlana Saarela [cre, aut], Sören Holm [aut], Zhiqiang Yang [aut], Wilmer Prentius [ctb]
Maintainer: Svetlana Saarela <admin@svetlanasaarela.com>
Repository: CRAN
Date/Publication: 2020-05-06 07:10:02 UTC

Class HMB

Description

Class HMB is the base class for the HMB-package

See Also

hmb, ghmb, tsmb, gtsmb


Sample Data for HMB package

Description

A data frame with 100000 records.

Names are GSV: hMAX: h80: CRR: pVeg: B20: B30: B50:


Class SummaryHMB

Description

Class SummaryHMB defines summary information for HMB object.


Method getSpec

Description

Get model specifications of HMB-class object

Usage

getSpec(obj)

## S4 method for signature 'HMB'
getSpec(obj)

Arguments

obj

Object of class HMB

Value

A list containing the estimated parameters, together with model arguments

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

hmb_model = hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
getSpec(hmb_model)

Generalized Hierarchical Model-Based estimation method

Description

Generalized Hierarchical Model-Based estimation method

Usage

ghmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Sigma_Sa)

Arguments

y_S

Response object that can be coerced into a column vector. The _S denotes that y is part of the sample S, with N_S \le N_{Sa} \le N_U.

X_S

Object of predictors variables that can be coerced into a matrix. The rows of X_S correspond to the rows of y_S.

X_Sa

Object of predictor variables that can be coerced into a matrix. The set Sa is the intermediate sample.

Z_Sa

Object of predictor variables that can be coerced into a matrix. The set Sa is the intermediate sample, and the Z-variables often some sort of auxilairy, inexpensive data. The rows of Z_Sa correspond to the rows of X_Sa

Z_U

Object of predictor variables that can be coerced into a matrix. The set U is the universal population sample.

Omega_S

The covariance structure of \boldsymbol{\epsilon}_{S}, up to a constant.

Sigma_Sa

The covariance structure of \boldsymbol{u}_{Sa}, up to a constant.

Details

The GHMB assumes two models

y = \boldsymbol{x} \boldsymbol{\beta} + \epsilon

\boldsymbol{x} \boldsymbol{\beta} = \boldsymbol{z} \boldsymbol{\alpha} + \boldsymbol{u}

\epsilon \perp u

For a sample from the superpopulation, the GHMB assumes

E(\boldsymbol{\epsilon}) = \mathbf{0}, E(\boldsymbol{\epsilon} \boldsymbol{\epsilon}^T) = \omega^2 \boldsymbol{\Omega}

E(\boldsymbol{u}) = \mathbf{0}, E(\boldsymbol{u} \boldsymbol{u}^T) = \sigma^2 \boldsymbol{\Sigma}

Value

A fitted object of class HMB.

References

Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., Petersson, H., Prentius, W., Patterson, P.L., Næsset, E., Gregoire, T.G. & Ståhl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data, Remote Sensing, 10(11), 1832.

See Also

summary, getSpec.

Examples

pop_U    = sample(nrow(HMB_data), 20000)
pop_Sa   = sample(pop_U, 2500)
pop_S    = sample(pop_U, 300)

y_S      = HMB_data[pop_S, "GSV"]
X_S      = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa     = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa     = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U      = HMB_data[pop_U, c("B20", "B30", "B50")]

Omega_S  = diag(1, nrow(X_S))
Sigma_Sa = diag(1, nrow(Z_Sa))

ghmb_model = ghmb(
  y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Sigma_Sa)
ghmb_model

Generalized Two-Staged Model-Based estmation

Description

Generalized Two-Staged Model-Based estmation

Usage

gtsmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Phis_Sa)

Arguments

y_S

Response object that can be coersed into a column vector. The _S denotes that y is part of the sample S, with N_S \le N_{Sa} \le N_U.

X_S

Object of predictors variables that can be coersed into a matrix. The rows of X_S correspond to the rows of y_S.

X_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample.

Z_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample, and the Z-variables often some sort of auxilairy, inexpensive data. The rows of Z_Sa correspond to the rows of X_Sa

Z_U

Object of predictor variables that can be coresed into a matrix. The set U is the universal population sample.

Omega_S

The covariance structure of \boldsymbol{\epsilon}_{S}, up to a constant.

Phis_Sa

A 3D array, where the third dimension corresponds to the covariance structure of E(\boldsymbol{\xi}_{k,Sa} \boldsymbol{\xi}_{j,Sa}^T), in the order k=1, \ldots, p, j=1, \ldots k. For p = 3, the order (k,j) will thus be (1,1), (2,1), (2,2), (3,1), (3,2), (3,3).

Details

The GTSMB assumes the superpopulations

y = \boldsymbol{x} \boldsymbol{\beta} + \epsilon

x_k = \boldsymbol{z} \boldsymbol{\gamma}_k + \xi_k

\epsilon \perp \xi_k

For a sample from the superpopulation, the GTSMB assumes

E(\boldsymbol{\epsilon}) = \mathbf{0}, E(\boldsymbol{\epsilon} \boldsymbol{\epsilon}^T) = \omega^2 \boldsymbol{\Omega}

E(\boldsymbol{\xi}_k) = \mathbf{0}, E(\boldsymbol{\xi}_k \boldsymbol{\xi}_j^T) = \theta_{\Phi,k,j}^2 \boldsymbol{\Phi}_{k,j}, \theta_{\Phi,k,j}^2 \boldsymbol{\Phi}_{k,j} = \theta_{\Phi,j,k}^2 \boldsymbol{\Phi}_{j,k}

Value

A fitted object of class HMB.

References

Holm, S., Nelson, R. & Ståhl, G. (2017) Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar. Remote Sensing of Environment, 197, 85–97.

Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., Petersson, H., Prentius, W., Patterson, P.L., Næsset, E., Gregoire, T.G. & Ståhl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data, Remote Sensing, 10(11), 1832.

See Also

summary, getSpec.

Examples

pop_U   = sample(nrow(HMB_data), 20000)
pop_Sa  = sample(pop_U, 500)
pop_S   = sample(pop_U, 100)

y_S     = HMB_data[pop_S, "GSV"]
X_S     = HMB_data[pop_S, c("hMAX", "h80", "CRR")]
X_Sa    = HMB_data[pop_Sa, c("hMAX", "h80", "CRR")]
Z_Sa    = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U     = HMB_data[pop_U, c("B20", "B30", "B50")]

Omega_S = diag(1, nrow(X_S))
Phis_Sa = array(0, c(nrow(X_Sa), nrow(X_Sa), ncol(X_Sa) * (ncol(X_Sa) + 1) / 2))
Phis_Sa[, , 1] = diag(1, nrow(X_Sa)) # Phi(1,1)
Phis_Sa[, , 2] = diag(1, nrow(X_Sa)) # Phi(2,1)
Phis_Sa[, , 3] = diag(1, nrow(X_Sa)) # Phi(2,2)
Phis_Sa[, , 4] = diag(1, nrow(X_Sa)) # Phi(3,1)
Phis_Sa[, , 5] = diag(1, nrow(X_Sa)) # Phi(3,2)
Phis_Sa[, , 6] = diag(1, nrow(X_Sa)) # Phi(3,3)

gtsmb_model = gtsmb(y_S, X_S, X_Sa, Z_Sa, Z_U, Omega_S, Phis_Sa)
gtsmb_model

Hierarchical Model-Based estmation

Description

Hierarchical Model-Based estmation

Usage

hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)

Arguments

y_S

Response object that can be coersed into a column vector. The _S denotes that y is part of the sample S, with N_S \le N_{Sa} \le N_U.

X_S

Object of predictors variables that can be coersed into a matrix. The rows of X_S correspond to the rows of y_S.

X_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample.

Z_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample, and the Z-variables often some sort of auxilairy, inexpensive data. The rows of Z_Sa correspond to the rows of X_Sa

Z_U

Object of predictor variables that can be coresed into a matrix. The set U is the universal population sample.

Details

The HMB assumes two models

y = \boldsymbol{x} \boldsymbol{\beta} + \epsilon

\boldsymbol{x} \boldsymbol{\beta} = \boldsymbol{z} \boldsymbol{\alpha} + u

\epsilon \perp u

For a sample from the superpopulation, the HMB assumes

E(\boldsymbol{\epsilon}) = \mathbf{0}, E(\boldsymbol{\epsilon} \boldsymbol{\epsilon}^T) = \omega^2 \mathbf{I}

E(\boldsymbol{u}) = \mathbf{0}, E(\boldsymbol{u} \boldsymbol{u}^T) = \sigma^2 \mathbf{I}

Value

A fitted object of class HMB.

References

Saarela, S., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T.G., Nelson, R.F. & Ståhl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information, Annals of Forest Science, 73(4), 895-910.

Saarela, S., Holm, S., Healey, S.P., Andersen, H.-E., Petersson, H., Prentius, W., Patterson, P.L., Næsset, E., Gregoire, T.G. & Ståhl, G. (2018). Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data, Remote Sensing, 10(11), 1832.

See Also

summary, getSpec.

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

hmb_model = hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
hmb_model

Method show

Description

Display model outputs

Display model summary properties

Usage

## S4 method for signature 'HMB'
show(object)

## S4 method for signature 'SummaryHMB'
show(object)

Arguments

object

Object of class HMB

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

hmb_model = hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
show(hmb_model)
pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

hmb_model = hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
show(summary(hmb_model))

Method summary

Description

Summary of HMB model

Usage

summary(obj)

## S4 method for signature 'HMB'
summary(obj)

Arguments

obj

Object of class HMB

Value

Summary of HMB model.

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

S_Sa_map = matrix(pop_S, nrow = nrow(X_S), ncol = nrow(X_Sa))
S_Sa_map = t(apply(S_Sa_map, 1, function(x) {
  return(x == pop_Sa)
})) * 1

hmb_model = hmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
summary(hmb_model)

Two-staged Model-Based estmation

Description

Two-staged Model-Based estmation

Usage

tsmb(y_S, X_S, X_Sa, Z_Sa, Z_U)

Arguments

y_S

Response object that can be coersed into a column vector. The _S denotes that y is part of the sample S, with N_S \le N_{Sa} \le N_U.

X_S

Object of predictors variables that can be coersed into a matrix. The rows of X_S correspond to the rows of y_S.

X_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample.

Z_Sa

Object of predictor variables that can be coresed into a matrix. The set Sa is the intermediate sample, and the Z-variables often some sort of auxilairy, inexpensive data. The rows of Z_Sa correspond to the rows of X_Sa

Z_U

Object of predictor variables that can be coresed into a matrix. The set U is the universal population sample.

Details

The TSMB assumes the superpopulations

y = \boldsymbol{x}^T \boldsymbol{\beta} + \epsilon

x_k = \boldsymbol{z}^T \boldsymbol{\gamma}_k + \xi_k

\epsilon \perp \xi_k

For a sample from the superpopulation, the TSMB assumes

E(\boldsymbol{\epsilon}) = \mathbf{0}, E(\boldsymbol{\epsilon} \boldsymbol{\epsilon}^T) = \omega^2 \mathbf{I}

E(\boldsymbol{\xi}_k) = \mathbf{0}, E(\boldsymbol{\xi}_k \boldsymbol{\xi}_j^T) = \phi_{k,j}^2 \mathbf{I}

Value

A fitted object of class HMB.

References

Saarela, S., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T.G., Nelson, R.F. & Ståhl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of Forest Science, 73(4), 895-910.

See Also

summary, getSpec.

Examples

pop_U  = sample(nrow(HMB_data), 20000)
pop_Sa = sample(pop_U, 5000)
pop_S  = sample(pop_U, 300)

y_S    = HMB_data[pop_S, "GSV"]
X_S    = HMB_data[pop_S, c("hMAX", "h80", "CRR", "pVeg")]
X_Sa   = HMB_data[pop_Sa, c("hMAX", "h80", "CRR", "pVeg")]
Z_Sa   = HMB_data[pop_Sa, c("B20", "B30", "B50")]
Z_U    = HMB_data[pop_U, c("B20", "B30", "B50")]

tsmb_model = tsmb(y_S, X_S, X_Sa, Z_Sa, Z_U)
tsmb_model

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.